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Record W4313490194 · doi:10.1108/ijotb-04-2022-0077

Getting to diversity: an examination of the antecedents and outcomes of resistance to diversity-related organizational change

2023· article· en· W4313490194 on OpenAlexaff
Angela Workman-Stark

Bibliographic record

VenueInternational Journal of Organization Theory and Behavior · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsAthabasca University
Fundersnot available
KeywordsResistance (ecology)Diversity (politics)Organizational justiceSocial psychologyPsychologyOrganizational changeValue (mathematics)Economic JusticeOriginalityRace (biology)Organizational commitmentSociologyPublic relationsPolitical scienceGender studies

Abstract

fetched live from OpenAlex

Purpose The purpose of this study was to investigate the contributors to individual resistance to diversity-related organizational change (DROC) and how it might be reduced. Design/methodology/approach From survey data collected through three separate samples of the US population, the study tested the antecedents and outcomes of resistance to DROC and the moderating effect of organizational justice on these relationships. Findings Findings reveal that attitudes about workplace diversity are influenced by individual factors (sex and race), which in turn are significantly related to individual resistance to DROC. Independently, organizational justice moderated the effects of employee attitudes and perceived threats on resistance to DROC, suggesting that resistance is increased when employees perceive they are treated justly. Originality/value This is the first known study to investigate resistance to DROC as well as its potential antecedents and outcomes. Findings suggest that organizational justice is an important consideration in implementing DROC.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.311
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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